Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records
المؤلفون المشاركون
Taewijit, Siriwon
Theeramunkong, Thanaruk
Ikeda, Mitsuru
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-21، 21ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-09-26
دولة النشر
مصر
عدد الصفحات
21
التخصصات الرئيسية
الملخص EN
Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes.
Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts.
Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language.
In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation.
The multiple-instance learning with expectation-maximization method is employed to estimate model parameters.
The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time.
By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data.
Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Taewijit, Siriwon& Theeramunkong, Thanaruk& Ikeda, Mitsuru. 2017. Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-21.
https://search.emarefa.net/detail/BIM-1181190
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Taewijit, Siriwon…[et al.]. Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records. Journal of Healthcare Engineering No. 2017 (2017), pp.1-21.
https://search.emarefa.net/detail/BIM-1181190
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Taewijit, Siriwon& Theeramunkong, Thanaruk& Ikeda, Mitsuru. Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-21.
https://search.emarefa.net/detail/BIM-1181190
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1181190
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر